Archive for latent variable models

averaged acceptance ratios

Posted in Statistics with tags , , , , , , , , , , , , , on January 15, 2021 by xi'an

In another recent arXival, Christophe Andrieu, Sinan Yıldırım, Arnaud Doucet, and Nicolas Chopin study the impact of averaging estimators of acceptance ratios in Metropolis-Hastings algorithms. (It is connected with the earlier arXival rephrasing Metropolis-Hastings in terms of involutions discussed here.)

“… it is possible to improve performance of this algorithm by using a modification where the acceptance ratio r(ξ) is integrated with respect to a subset of the proposed variables.”

This interpretation of the current proposal makes it a form of Rao-Blackwellisation, explicitly mentioned on p.18, where, using a mixture proposal, with an adapted acceptance probability, it depends on the integrated acceptance ratio only. Somewhat magically using this ratio and its inverse with probability ½. And it increases the average Metropolis-Hastings acceptance probability (albeit with a larger number of simulations). Since the ideal averaging is rarely available, the authors implement a Monte Carlo averaging version. With applications to the exchange algorithm and to reversible jump MCMC. The major application is to pseudo-marginal settings with a high complexity (in the number T of terms) and where the authors’ approach does scale efficiently with T. There is even an ABC side to the story as one illustration is made of the ABC approximation to the posterior of an α-stable sample. As an encompassing proposal for handling Metropolis-Hastings environments with latent variables and several versions of the acceptance ratios, this is quite an interesting paper that I think we will study in further detail with our students.

mining gold [ABC in PNAS]

Posted in Books, Statistics with tags , , , , , , , , , , , on March 13, 2020 by xi'an

Johann Brehmer and co-authors have just published a paper in PNAS entitled “Mining gold from implicit models to improve likelihood-free inference”. (Besides the pun about mining gold, the paper also involves techniques named RASCAL and SCANDAL, respectively! For Ratio And SCore Approximate Likelihood ratio and SCore-Augmented Neural Density Approximates Likelihood.) This setup is not ABC per se in that their simulator is used both to generate training data and construct a tractable surrogate model. Exploiting Geyer’s (1994) classification trick of expressing the likelihood ratio as the optimal classification ratio when facing two equal-size samples from one density and the other.

“For all these inference strategies, the augmented data is particularly powerful for enhancing the power of simulation-based inference for small changes in the parameter θ.”

Brehmer et al. argue that “the most important novel contribution that differentiates our work from the existing methods is the observation that additional information can be extracted from the simulator, and the development of loss functions that allow us to use this “augmented” data to more efficiently learn surrogates for the likelihood function.” Rather than starting from a statistical model, they also seem to use a scientific simulator made of multiple layers of latent variables z, where

x=F⁰(u⁰,z¹,θ), z¹=G¹(u¹,z²), z²=G¹(u²,z³), …

although they also call the marginal of x, p(x|θ), an (intractable) likelihood.

“The integral of the log is not the log of the integral!”

The central notion behind the improvement is a form of Rao-Blackwellisation, exploiting the simulated z‘s. Joint score functions and joint likelihood ratios are then available. Ignoring biases, the authors demonstrate that the closest approximation to the joint likelihood ratio and the joint score function that only depends on x is the actual likelihood ratio and the actual score function, respectively. Which sounds like an older EM result, except that the roles of estimate and target quantity are somehow inverted: one is approximating the marginal with the joint, while the marginal is the “best” approximation of the joint. But in the implementation of the method, an estimate of the (observed and intractable) likelihood ratio is indeed produced towards minimising an empirical loss based on two simulated samples. Learning this estimate ê(x) then allows one to use it for the actual data. It however requires fitting a new ê(x) for each pair of parameters. Providing as well an estimator of the likelihood p(x|θ). (Hence the SCANDAL!!!) A second type of approximation of the likelihood starts from the approximate value of the likelihood p(x|θ⁰) at a fixed value θ⁰ and expands it locally as an exponential family shift, with the score t(x|θ⁰) as sufficient statistic.

I find the paper definitely interesting even though it requires the representation of the (true) likelihood as a marginalisation over multiple layers of latent variables z. And does not provide an evaluation of the error involved in the process when the model is misspecified. As a minor supplementary appeal of the paper, the use of an asymmetric Galton quincunx to illustrate an intractable array of latent variables will certainly induce me to exploit it in projects and courses!

[Disclaimer: I was not involved in the PNAS editorial process at any point!]

unbiased product of expectations

Posted in Books, Statistics, University life with tags , , , , , , , , on August 5, 2019 by xi'an

m_biomet_106_2coverWhile I was not involved in any way, or even aware of this research, Anthony Lee, Simone Tiberi, and Giacomo Zanella have an incoming paper in Biometrika, and which was partly written while all three authors were at the University of Warwick. The purpose is to design an efficient manner to approximate the product of n unidimensional expectations (or integrals) all computed against the same reference density. Which is not a real constraint. A neat remark that motivates the method in the paper is that an improved estimator can be connected with the permanent of the n x N matrix A made of the values of the n functions computed at N different simulations from the reference density. And involves N!/ (N-n)! terms rather than N to the power n. Since it is NP-hard to compute, a manageable alternative uses random draws from constrained permutations that are reasonably easy to simulate. Especially since, given that the estimator recycles most of the particles, it requires a much smaller version of N. Essentially N=O(n) with this scenario, instead of O(n²) with the basic Monte Carlo solution, towards a similar variance.

This framework offers many applications in latent variable models, including pseudo-marginal MCMC, of course, but also for ABC since the ABC posterior based on getting each simulated observation close enough from the corresponding actual observation fits this pattern (albeit the dependence on the chosen ordering of the data is an issue that can make the example somewhat artificial).

interdependent Gibbs samplers

Posted in Books, Statistics, University life with tags , , , , , , on April 27, 2018 by xi'an

Mark Kozdoba and Shie Mannor just arXived a paper on an approach to accelerate a Gibbs sampler. With title “interdependent Gibbs samplers“. In fact, it presents rather strong similarities with our SAME algorithm. More of the same, as Adam Johanssen (Warwick) entitled one of his papers! The paper indeed suggests multiplying replicas of latent variables (e.g., an hidden path for an HMM) in an artificial model. And as in our 2002 paper, with Arnaud Doucet and Simon Godsill, the focus here is on maximum likelihood estimation (of the genuine parameters, not of the latent variables). Along with argument that the resulting pseudo-posterior is akin to a posterior with a powered likelihood. And a link with the EM algorithm. And an HMM application.

“The generative model consist of simply sampling the parameters ,  and then sampling m independent copies of the paths”

If anything this proposal is less appealing than SAME because it aims directly at the powered likelihood, rather than utilising an annealed sequence of powers that allows for a primary exploration of the whole parameter space before entering the trapping vicinity of a mode. Which makes me fail to catch the argument from the authors that this improves Gibbs sampling, as a more acute mode has on the opposite the dangerous feature of preventing visits to other modes. Hence the relevance to resort to some form of annealing.

As already mused upon in earlier posts, I find it most amazing that this technique has been re-discovered so many times, both in statistics and in adjacent fields. The idea of powering the likelihood with independent copies of the latent variables is obviously natural (since a version pops up every other year, always under a different name), but earlier versions should eventually saturate the market!

running ABC when the likelihood is available

Posted in Statistics with tags , , , , , on September 19, 2017 by xi'an

Today I refereed a paper where the authors used ABC to bypass convergence (and implementation) difficulties with their MCMC algorithm. And I am still pondering whether or not this strategy makes sense. If only because ABC needs to handle the same complexity and the same amount of parameters as an MCMC algorithm. While shooting “in the dark” by using the prior or a coarse substitute to the posterior. And I wonder at the relevance of simulating new data when the [true] likelihood value [at the observed data] can be computed. This would sound to me like the relevant and unique “statistics” worth considering…